Brown carbon (BrC) has been proposed as an important driving factor in climate change due to its light absorption properties. However, our understanding of BrC’s chemical and optical properties are inadequate, particularly at remote regions. This study conducts a comprehensive investigation of BrC aerosols in summer (Aug. 2013) and winter (Jan. 2014) at Southeast Tibetan Plateau, which is ecologically fragile and sensitive to global warming. The concentrations of methanol-soluble BrC (MeS-BrC) are approximately twice of water-soluble BrC (WS-BrC), demonstrating the environmental importance of water-insoluble BrC are previously underestimated with only WS-BrC considered. The mass absorption efficiency of WS-BrC (0.27–0.86 m² g⁻¹) is lower than those in heavily polluted South Asia, indicating a distinct contrast between the two sides of Himalayas. Fluorescence reveals that the absorption of BrC is mainly attributed to humic-like and protein-like substances, which broaden the current knowledge of BrC’s chromophores. Combining organic tracer, satellite MODIS data and air-mass backward trajectory analysis, this study finds BrC is mainly derived from bioaerosols and secondary formation in summer, while long-range transport of biomass burning emissions in winter. Our study provides new insights into the optical and chemical properties of BrC, which may have implications for environmental effect and sources of organic aerosols.

This study employs the grain size distributions and the concentrations and isotopic compositions of Sr, Nd, and Pb in the precipitation samples collected from the Laohugou Glacier (LHG) in northeastern Tibetan Plateau (TP) during August 2014–2015 to investigate seasonal variability in the insoluble precipitation particle sources. Fine dust particle (0.57–27 μm) depositions dominated in autumn and winter, whereas both fine and coarse dust particle (27–100 μm) depositions were found in spring and summer. Furthermore, the concentrations of Sr, Nd, and Pb also varied seasonally—the highest and lowest Sr and Nd concentrations were recorded in spring and autumn, respectively, whereas the highest and lowest Pb concentrations were recorded in winter and summer, respectively. The Sr and Nd isotopes revealed that the dust in the winter precipitation originated predominately from the Taklimakan Desert and that in spring originated from the Badain Jaran and Qaidam deserts. The precipitation residues in summer were derived from a complex mixture of dust sources from the Gobi and other large deserts in northwest China. Autumn residues were predominately sourced from local soil near the LHG as well as from the Qaidam Basin and the northern TP surface soil. The Taklimakan, long suspected as a major source of long-range transported dust, was an insignificant contributor to the precipitation over LHG during spring, summer, and autumn. Further, the Pb isotopic ratios indicated a primary impact of anthropogenic pollutants for most part of the year (except spring). Meteorological data and the MODIS AOD model are in good agreement with the results from the analyses of the Sr, Nd, and Pb isotopes for the LHG particle source, and further clarify the source regions. Thus, this study thus provides new evidence on the seasonal variability of the sources of the residual particles in remote glaciers in Central Asia.

Satellite-based aerosol optical depth (AOD) is now comprehensively applied to estimate ground-level concentrations of fine particulate matter (PM2.5). This study aimed to construct the AOD-PM2.5 estimation models over Taiwan. The AOD-PM2.5 modeling in Taiwan island is challenging owing to heterogeneous land use, complex topography, and humid tropical to subtropical climate conditions with frequent cloud cover and prolonged rainy season. The AOD retrievals from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites were combined with the meteorological variables from reanalysis data and high resolution localized land use variables to estimate PM2.5 over Taiwan island from 2005 to 2015. Ten-fold cross validation was carried out and the residuals of the estimation model at various locations and seasons are assessed. The cross validation (CV) R2 based on monitoring stations were 0.66 and 0.66, with CV root mean square errors of 14.0 μg/m3 (34%) and 12.9 μg/m3 (33%), respectively, for models based on Terra and Aqua AOD. The results provided PM2.5 estimations at locations without surface stations. The estimation revealed PM2.5 concentration hotspots in the central and southern part of the western plain areas, particularly in winter and spring. The annual average of estimated PM2.5 concentrations over Taiwan consistently declined during 2005–2015. The AOD-PM2.5 model is a reliable and validated method for estimating PM2.5 concentrations at locations without monitoring stations in Taiwan, which is crucial for epidemiological study and for the assessment of air quality control policy.

Ground fine particulate matter (PM2.5) concentrations at high spatial resolution are substantially required for determining the population exposure to PM2.5 over densely populated urban areas. However, most studies for China have generated PM2.5 estimations at a coarse resolution (≥10 km) due to the limitation of satellite aerosol optical depth (AOD) product in spatial resolution. In this study, the 3 km AOD data fused using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOD products were employed to estimate the ground PM2.5 concentrations over the Beijing-Tianjin-Hebei (BTH) region of China from January 2013 to December 2015. An improved geographically and temporally weighted regression (iGTWR) model incorporating seasonal characteristics within the data was developed, which achieved comparable performance to the standard GTWR model for the days with paired PM2.5- AOD samples (Cross-validation (CV) R2 = 0.82) and showed better predictive power for the days without PM2.5- AOD pairs (the R2 increased from 0.24 to 0.46 in CV). Both iGTWR and GTWR (CV R2 = 0.84) significantly outperformed the daily geographically weighted regression model (CV R2 = 0.66). Also, the fused 3 km AODs improved data availability and presented more spatial gradients, thereby enhancing model performance compared with the MODIS original 3/10 km AOD product. As a result, ground PM2.5 concentrations at higher resolution were well represented, allowing, e.g., short-term pollution events and long-term PM2.5 trend to be identified, which, in turn, indicated that concerns about air pollution in the BTH region are justified despite its decreasing trend from 2013 to 2015.

Urban heat island (UHI) can generate profound impacts on socioeconomics, human life, and the environment. Most previous studies have estimated UHI intensity using outdated urban extent maps to define urban and its surrounding areas, and the impacts of urban boundary expansion have never been quantified. Here, we assess the possible biases in UHI intensity estimates induced by outdated urban boundary maps using MODIS Land surface temperature (LST) data from 2009 to 2011 for China's 32 major cities, in combination with the urban boundaries generated from urban extent maps of the years 2000, 2005 and 2010. Our results suggest that it is critical to use concurrent urban extent and LST maps to estimate UHI at the city and national levels. Specific definition of UHI matters for the direction and magnitude of potential biases in estimating UHI intensity using outdated urban extent maps.

Particulate matter (PM10) is the key indicator of air quality index in Brunei Darussalam and the principal pollutant for haze related episodes in Southeast Asia. This study examined the temporal and spatial distribution of PM10 base on a long-term monitoring data (2009–2014) in order to identify the emission sources and favorable meteorological conditions for high PM10 concentrations across the country. PM10 concentrations measured at the various locations differ significantly but the general temporal characteristics show clear patterns of seasonal variations across the country with the highest concentrations recorded during the southwest monsoon. The high PM10 values defined in the study were not evenly distributed over the years but occurred mostly within the southwest monsoon months of June to September. Further investigations with bivariate polar concentrations plots and k-means clustering demonstrated the significant influence of Southeast Asian regional biomass fires on the high PM10 concentrations recorded across the country. The results of the polar plots and cluster analyses were further confirmed by the evaluations with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward air masses trajectories analysis and the Moderate Resolution Imaging Spectroradiometer (MODIS) fire records. Among the meteorological variables considered, temperature, rainfall and relative humidity were the most important meteorological variables that influence the concentration throughout the year. High PM10 values are associated with high temperatures and low amounts of rainfall and relative humidity. In addition, wind speed and direction also play significant role in the recorded high PM10 concentrations and were mainly responsible for its seasonality during the study period.

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only.

Agricultural residue burning is one of the major causes of greenhouse gas emissions and aerosols in the Indo-Ganges region. In this study, we characterize the fire intensity, seasonality, variability, fire radiative energy (FRE) and aerosol optical depth (AOD) variations during the agricultural residue burning season using MODIS data. Fire counts exhibited significant bi-modal activity, with peak occurrences during April–May and October–November corresponding to wheat and rice residue burning episodes. The FRE variations coincided with the amount of residues burnt. The mean AOD (2003–2008) was 0.60 with 0.87 (+1σ) and 0.32 (−1σ). The increased AOD during the winter coincided well with the fire counts during rice residue burning season. In contrast, the AOD-fire signal was weak during the summer wheat residue burning and attributed to dust and fossil fuel combustion. Our results highlight the need for ‘full accounting of GHG’s and aerosols’, for addressing the air quality in the study area.

In this analysis, the Aqua/MODIS aerosol optical thickness (AOD), Aura/OMI tropospheric NO2 and SO2 column concentration from 2006 to 2015 were used to statistically analyze the spatial distribution characteristics and variation trends of three polluted parameters from three temporal scales of monthly, seasonal and annual average. The results showed that the minimum values of NO2 and SO2 column concentrations both appeared in July and August, and the maximum values appeared in December and January, which was contrary to the variations in AOD. The highly polluted levels were mainly distributed in Shijiazhuang, Xingtai, and Yancheng cities of Hebei Province, and gradually transported to Zhengzhou, Henan Province, north and southwest of Shandong Province, and Tianjin, along the main line of Taiyuan-Linyi, Shanxi Province. AOD and NO2 had significant differences on the seasonal average scale, whereas SO2 had little changes. These pollutants had declined year by year since 2011, in the 10-year period, AOD and SO2 respectively decreased by 17.14% and 10.57%, and only NO2 rose from 8.69 × 1015 molecules/cm2 in 2006 to 9.10 × 1015 molecules/cm2 in 2015 with the increase rate of 4.79%. Integrated with MODIS-released fire products and the Multi-resolution Emission Inventory for China (MEIC), high AOD values in summer were usually accompanied by frequent biomass burning, and heavy heating demand of coal burning led to largest NO2 and SO2 levels in winter. Both inter-annual variations of MEIC NOx and OMI-observed NO2 responded to emission reductions of vehicle exhaustions positively, but vehicle population in Henan and Shandong provinces need to be further controlled. The significant decline of SO2 is mainly attributed to the enforcement of de-sulfurization devices in power plants. Our study found that in the treatment of complex atmospheric pollution, in addition to strict control of common sources of emissions from AOD, NO2 and SO2, it is also necessary to consider their individual characteristics.

An accurate estimation of PM2.5 (fine particulate matters with diameters ≤ 2.5 μm) concentration is critical for health risk assessment and generating air pollution control strategies. In this study, a hybrid remote sensing and machine learning approach, named RSRF model is proposed to estimate daily ground-level PM2.5 concentrations, which integrates Random Forest (RF), one of machine learning (ML) models, and aerosol optical depth (AOD), one of remote sensing (RS) products. The proposed RSRF model provides an opportunity for an adequate characterization of real-time spatiotemporal PM2.5 distributions at uninhabited places and complex surfaces. It also offers advantages in handling complicated non-linear relationships among a large number of meteorological, environmental and air pollutant factors, as well as ever-increasing environmental data sets. The applicability of the proposed RSRF model is tested in the Beijing-Tianjin-Hebei region (BTH region) during 2015–2017. Deep Blue (DB) AOD from Aqua-retrieved Collection 6.1 (C_61) aerosol products of Moderate Resolution Imaging Spectroradiometer (MODIS) is validated with Aerosol Robotic Network. The validation results indicate C_61 DB AOD has a high correlation with ground based AOD in the BTH region. The proposed RSRF model performed well in characterizing spatiotemporal variations of annual and seasonal PM2.5 concentrations. It not only is useful to quantify the relationships between PM2.5 and relevant factors such as DB AOD, meteorological and air pollutant variables, but also can provide decision support for air pollution control at a regional environment during haze periods.